MuEvo: LLM-Driven Evolution of Multi-Heuristic Ensemble

• URL: http://arxiv.org/abs/2608.03636v1
• Date: Tue, 04 Aug 2026 13:19:51 GMT
• Title: MuEvo: LLM-Driven Evolution of Multi-Heuristic Ensemble
• Abstract summary: We propose MuEvo, an LLM-driven framework for evolving ensembles under ensemble-level feedback.<n>We evaluate MuEvo on selection-heuristics and componentized ant colony optimization across four optimization domains.
• Score: 16.72462798926915
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language model-based automated heuristic design (LLM-AHD) has shown strong potential in discovering effective heuristics for combinatorial optimization problems. However, existing methods primarily optimize a single heuristic, whereas practical optimization frameworks often rely on multiple interacting components. Directly extending single-heuristic methods is challenging because early component selection can overlook components with late potential, while independent evolution ignores inter-component dependencies. We propose MuEvo, an LLM-driven framework for evolving heuristic ensembles under ensemble-level feedback. MuEvo combines Dynamic Component Management, which uses short-budget probing and a reversible lifecycle to revise component priorities throughout the search, with LLM-Driven Co-Evolution, which coordinates component populations through Multi-Ensemble Evaluation, Cross-Component Information Sharing, Relation-Guided Pair Evolution, and Adaptive Budget Allocation. We evaluate MuEvo on selection hyper-heuristics and componentized ant colony optimization across four combinatorial optimization domains. Results show that MuEvo consistently improves human-designed frameworks and outperforms representative multi-component extensions of state-of-the-art LLM-AHD methods, demonstrating its effectiveness across both controller-mediated heuristic pools and functionally differentiated algorithmic components.

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